MPB_2025v16n5

Molecular Plant Breeding 2025, Vol.16, No.5, 268-277 http://genbreedpublisher.com/index.php/mpb 277 Withanawasam D., Kommana M., Pulindala S., Eragam A., Moode V., Kolimigundla A., Puram R., Palagiri S., Balam R., and Vemireddy L., 2022, Improvement of grain yield under moisture and heat stress conditions through marker-assisted pedigree breeding in rice (Oryza sativa L.), Crop and Pasture Science, 73: 356-369. https://doi.org/10.1071/CP21410 Wu C., Luo J., and Xiao Y., 2024, Multi-omics assists genomic prediction of maize yield with machine learning approaches, Molecular Breeding, 44: 14. https://doi.org/10.1007/s11032-024-01454-z Wu Y., and Xie L., 2024, AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships, Computational and Structural Biotechnology Journal, 27: 265-277. https://doi.org/10.1016/j.csbj.2024.12.030 Xu Y., Zhao Y., Wang X., Ma Y., Li P., Yang Z., Zhang X., Xu C., and Xu S., 2020, Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice, Plant Biotechnology Journal, 19(2): 261-272. https://doi.org/10.1111/pbi.13458 Yadavalli V., Balakrishnan D., Surapaneni M., Addanki K., Mesapogu S., Beerelli K., Desiraju S., Voleti S., and Neelamraju S., 2022, Mapping QTLs for yield and photosynthesis-related traits in three consecutive backcross populations of Oryza sativa cultivar Cottondora Sannalu (MTU1010) and Oryza rufipogon, Planta, 256: 71. https://doi.org/10.1007/s00425-022-03983-3 Yan J., and Wang X., 2022, Machine learning bridges omics sciences and plant breeding, Trends in Plant Science, 28(2): 199-210. https://doi.org/10.1016/j.tplants.2022.08.018 Yang Y., Saand M., Huang L., Abdelaal W., Zhang J., Wu Y., Li J., Sirohi M., and Wang F., 2021, Applications of multi-omics technologies for crop improvement, Frontiers in Plant Science, 12: 563953. https://doi.org/10.3389/fpls.2021.563953 Yin M., Tong X., Yang J., Cheng Y., Zhou P., Li G., Wang Y., and Ying J., 2024, Dissecting the genetic basis of yield traits and validation of a novel quantitative trait locus for grain width and weight in rice, Plants, 13(6): 770. https://doi.org/10.3390/plants13060770 Yoosefzadeh-Najafabadi M., Rajcan I., and Eskandari M., 2022, Optimizing genomic selection in soybean: an important improvement in agricultural genomics, Heliyon, 8(11): e11873. https://doi.org/10.1016/j.heliyon.2022.e11873 Yu P., Ye C., Li L., Yin H., Wang Y., Zhang Z., Li W., Long Y., Hu X., Xiao J., Jia G., and Tian B., 2022, Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice, Molecular Breeding, 42: 16. https://doi.org/10.1007/s11032-022-01289-6 Zhang F., Wang C., Li M., Cui Y., Shi Y., Wu Z., Hu Z., Wang W., Xu J., and Li Z., 2021, The landscape of gene-CDS-haplotype diversity in rice (Oryza sativa L.): properties, population organization, footprints of domestication and breeding, and implications in genetic improvement, Molecular Plant, 14(5): 787-804. https://doi.org/10.1016/j.molp.2021.02.003 Zhang Y., Zhou J., Xu P., Li J., Deng X., Deng W., Yang Y., Yu Y., Pu Q., and Tao D., 2022, A genetic resource for rice improvement: introgression library of agronomic traits for all AA genome Oryza species, Frontiers in Plant Science, 13: 856514. https://doi.org/10.3389/fpls.2022.856514 Zhong H., Liu S., Sun T., Kong W., Deng X., Peng Z., and Li Y., 2021, Multi-locus genome-wide association studies for five yield-related traits in rice, BMC Plant Biology, 21: 364. https://doi.org/10.1186/s12870-021-03146-8

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